The proliferation of traffic and surveillance cameras has led to an increased demand for automated video analytics technologies. This has vaulted such technology to the forefront of computer vision research. Automated video analytics technologies have made real-world, real-time surveillance possible. However, such real-world scenarios present a number of challenges traditional object tracking system are not equipped to handle. For example, occlusion, changes in scene illumination, weather conditions, object appearance characteristics, and camera shake cause known tracking methods and systems to fail.
Significant research efforts have been devoted to improving traffic monitoring and surveillance systems. However, environments encountered in real-world traffic and surveillance situations create unique, and as yet unsolved, problems. Prior art efforts are typically limited in scope based on the directions and speeds at which objects move. Thus, such methods are inefficient and not sufficiently robust. In particular, prior art methods and systems fail to adequately account for fluctuations in speed, varying weather conditions, times of day, traffic conditions, and other such factors. Therefore, there is a need for robust and computationally efficient methods and systems that account for, and are adaptable to, variations in the scene being monitored.